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dc.contributor.authorMothukuri S.K.P.
dc.contributor.authorTejas R.
dc.contributor.authorPatil S.
dc.contributor.authorDarshan V.
dc.contributor.authorKoolagudi S.G.
dc.identifier.citationCommunications in Computer and Information Science , Vol. 1209 CCIS , , p. 198 - 207en_US
dc.description.abstractIn this paper, a smart automatic traffic sign recognition system is proposed. This signboard recognition system plays a vital role in the automated driving system of transport vehicles. The model is built based on convolutional neural network. The German Traffic Sign Detection Benchmark (GTSDB), a standard open-source segmented image dataset with forty-three different signboard classes is considered for experimentation. Implementation of the system is highly focused on processing speed and classification accuracy. These aspects are concentrated, such that the built model is suitable for real-time automated driving systems. Similar experiments are carried in comparison with the pre-trained convolution models. The performance of the proposed model is better in the aspects of fast responsive time. © Springer Nature Singapore Pte Ltd. 2020.en_US
dc.titleEfficient Traffic Signboard Recognition System Using Convolutional Networksen_US
dc.typeConference Paperen_US
Appears in Collections:2. Conference Papers

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